We introduce Med-CTX, a fully transformer based multimodal framework for explainable breast cancer ultrasound segmentation. We integrate clinical radiology reports to boost both performance and interpretability. Med-CTX achieves exact lesion delineation by using a dual-branch visual encoder that combines&#...

This invited paper is the result of an invitation to write a retrospective article on a “TSE most influential paper” as part of the journal's 50th anniversary. The objective is to reflect on the progress of software engineering prediction research using the lens of a ...

Cellular myelocytomatosis oncogene (MYC) transcription factors are encoded by a family of genes that include the prototype member MYC, MYCN and MYCL, and most human cancers display expression alterations of MYC genes. MYC is regulated at multiple levels, and its stability and activity&#...

Corruption proves to be a complex issue, as it negatively affects all branches of social and political life. Corruption practices compromise trust in the political system, as well as hindering economic development (including investment flows) and preventing the recognition and the enjoyment&...

This special issue is concerned with a timely and seemingly neutralized issue; internationalization of higher education institutions and the making and unmaking of the “international” subject in the UK and across different social-cultural, national and political contexts. Our international/transnation...

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Multimodal object detection in remote sensing imagery has achieved remarkable performance, primarily owing to its ability to exploit complementary information from multiple modalities. However, most existing methods often suffer from substantial performance degradation under weakly aligned conditions, primarily...

A search is reported for charge-parity <i>CP>/i> violation in D° → K<sub0</sub>^SK<sub0</sub>^S decays, using data collected in proton–proton collisions at √𝒔 = 13 TeV recorded by the CMS experiment in 2018. The analysis uses a...

Accurate forecasting of power production and consumption is crucial for optimizing smart grid operations, especially with the growing integration of renewable energy sources, and minimizing CO₂ emissions. This study develops GridNet, a deep learning-based model for forecasting next-hour electricity co...